An Assessment of the Attention Demands during Random- and Blocked-Practice Schedules
Bibliographic record
Abstract
The reported study used the dual-task methodology to assess the attention demands associated with high and low contextual interference (CI) practice environments. Two specific issues were addressed. First, is there a difference in the attention demands during random and blocked schedules of practice? Second, what is the time course of any differential attention demands that emerge during random and blocked training? In order to address these questions two specific temporal loci were probed during practice: a pre-response interval and the inter-trial interval. It was assumed that the pre-response interval contained the reconstructive activity that is central to the reconstruction position. In contrast, the inter-trial interval has been interpreted in previous work to be the interval in which critical intra- and inter-item processing is performed during random practice. The data revealed a typical CI effect for the primary key-pressing task. Specifically, blocked-practice participants displayed superior performance during training but performed less well than the random-practice individuals at the time of retention. The poorer acquisition performance of the random-practice participants was associated with higher cognitive demand during both the pre-response and the inter-trial intervals than that of individuals assigned to blocked practice. The greater attention demands for random-practice individuals are discussed with respect to processes that might occur in both the pre-response and the inter-trial intervals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".